Search

Rabies, caused by rabies virus (RABV), is an ancient zoonosis and still a major public health problem for humans, especially in developing countries. RABV can be recognized by specific innate recognition receptors, resulting in the production of hundreds of interferon-stimulated genes (ISGs), which can inhibit viral replication at different stages. Interferon-inducible GTPase 1 (IIGP1) is a mouse-specific ISG and belongs to the immunity-related GTPases (IRGs) family. IIGP is reported to constrain intracellular parasite infection by disrupting the parasitophorous vacuole membrane. However, the role of IIGP1 in restricting viral replication has not been reported. In this present study, we found that IIGP1 was upregulated in cells and mouse brains upon RABV infection. Overexpression of IIGP1 limited RABV replication in cell lines and reduced viral pathogenicity in a mouse model. Consistently, deficiency of IIGP1 enhanced RABV replication in different parts of mouse brains. Furthermore, we found that IIGP1 could interact with RABV phosphoprotein (P protein). Mutation and immunoprecipitation analyses revealed that the Y128 site of P protein is critical for its interaction with IIGP1. Further study demonstrated that this interaction impeded the dimerization of P protein and thus suppressed RABV replication. Collectively, our findings for the first reveal a novel role of IIGP1 in restricting a typical neurotropic virus, RABV, which will provide fresh insight into the function of this mouse-specific ISG. IMPORTANCE Interferon and its downstream products, ISGs, are essential in defending against pathogen invasion. One of the ISGs, IIGP1, has been found to constrain intracellular parasite infection by disrupting their vacuole membranes. However, the role of IIGP1 in limiting viral infection is unclear. In this study, we show that infection with a typical neurotropic virus, RABV, can induce upregulation of IIGP1, which, in turn, suppresses RABV by interacting with its phosphoprotein (P protein) and thus blocking the dimerization of P protein. Our study provides the first evidence that IIGP1 functions in limiting viral infection and provides a basis for comprehensive understanding of this important ISG.

Purpose: The aims of the present study were to explore immune-related genes (IRGs) in stage IV colorectal cancer (CRC) and construct a prognostic risk score model to predict patient overall survival (OS), providing a reference for individualized clinical treatment. Methods: High-throughput RNA-sequencing, phenotype, and survival data from patients with stage IV CRC were downloaded from TCGA. Candidate genes were identified by screening for differentially expressed IRGs (DE-IRGs). Univariate Cox regression, LASSO, and multivariate Cox regression analyses were used to determine the final variables for construction of the prognostic risk score model. GSE17536 from the GEO database was used as an external validation dataset to evaluate the predictive power of the model. Results: A total of 770 candidate DE-IRGs were obtained, and a prognostic risk score model was constructed by variable screening using the following 12 genes: FGFR4, LGR6, TRBV12-3, NUDT6, MET, PDIA2, ORM1, IGKV3D-20, THRB, WNT5A, FGF18, and CCR8. In the external validation set, the survival prediction C-index was 0.685, and the AUC values were 0.583, 0.731, and 0.837 for 1-, 2- and 3-year OS, respectively. Univariate and multivariate Cox regression analyses demonstrated that the risk score model was an independent prognostic factor for patients with stage IV CRC. High- and low-risk patient groups had significant differences in the expression of checkpoint coding genes (ICGs). Conclusion: The prognostic risk score model for stage IV CRC developed in the present study based on immune-related genes has acceptable predictive power, and is closely related to the expression of ICGs.

BACKGROUND Immune system instability and poor prognosis are the two major clinical performance of hepatocellular carcinoma (HCC). Abnormal expression of MiR-424-5p has been reported to accelerate the progression of liver cancer, but it mediated immune cell infiltration imbalance is still unknown. We aim to mine the immune-related genes (IRGs) targeted by miR-424-5p and construct a multi-gene signature to improve the prognostic prediction of HCC. METHODS The HCC-related data of the cancer genome atlas (TCGA) database and the GSE14520 dataset of the gene expression omnibus (GEO) database were downloaded as the discovery dataset and the validation dataset, respectively. Weighted gene co-expression network analysis (WGCNA), the deconvolution algorithm of CIBERSORT and LASSO algorithm participated in the identification of IRGs and the development of prognostic signature and nomogram. RESULTS Our study found that the abundance of macrophages M0, M1 and M2 are all drastically changed during the cancerous process. A total of 920 macrophages infiltration-related LRGs were identified and a novel 4-gene signature (CDCA8, CBX2, UCK2 and SOCS2) with superior prognostic independence was established. The prognostic signature based risk score has superior capability to identify high-risk patients and predict overall survival (p

Purpose: Stomach adenocarcinoma (STAD) is one of the most common malignant tumors, and its occurrence and prognosis are closely related to inflammation. The aim of the present study was to identify gene signatures and construct an immune-related gene (IRG) prognostic model in STAD using bioinformatics analysis. Methods: RNA sequencing data from healthy samples and samples with STAD, IRGs, and transcription factors were analyzed. The hub IRGs were identified using univariate and multivariate Cox regression analyses. Using the hub IRGs, we constructed an IRG prognostic model. The relationships between IRG prognostic models and clinical data were tested. Results: A total of 289 differentially expressed IRGs and 20 prognostic IRGs were screened with a threshold of P Conclusions: Our results provide novel insights into the pathogenesis of STAD. An IRG prognostic model based on seven IRGs exhibited the predictive value, and have potential application value in clinical decision-making and individualized treatment.

Liver hepatocellular carcinoma (LIHC) is the most prevalent primary cancer of the liver, and immune-related genes (IRGs) regulate its development. So far, there is still no precise biomarker that predicts response to immunotherapy in LIHC. Therefore, this research seeks to identify immunogenic prognostic biomarkers and explore potential predictors for the efficacy of anti-PD-1/PD-L1 therapies in LIHC. The clinical data and gene expression profiles of patients diagnosed with LIHC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Moreover, IRGs were obtained from the ImmPort database. We discovered 35 IRGs that were differentially expressed between LIHC tissues and corresponding normal tissues. Through univariate Cox regression analysis, eight prognostic differentially expressed IRGs (PDEIRGs) were identified. Further, three optimal PDEIRGs (BIRC5, LPA, and ROBO1) were identified and used to construct a prognostic risk signature of LIHC patients via multivariate Cox regression analysis. The signature was validated by ROC curves. Subsequently, based on gene set enrichment analysis (GSEA) analysis, two out of the three optimal PDEIRGs (BIRC5 and LPA) were significantly enriched in the mismatch repair (MMR) pathway. Moreover, the two PDEIRGs (BIRC5 and LPA) were significantly correlated with the expression of genes related to mismatch repair (MLH1, MSH2, MSH6, and PMS2). Furthermore, correlations between the two PDEIRGs (BIRC5 and LPA) and immune checkpoints of cancer treatment (such as CTLA4, PD-1, and PD-L1) were demonstrated. Hyperprogressive disease (HPD) is a novel pattern of tumor progression which has a close relationship with immune checkpoint inhibitors (ICIs) utilization. MDM2 family amplification might promote the HPD phenomenon. Finally, we found a positive regulatory relationship between HPD related gene (MDM2) and BIRC5. Notably, MDM2 can either interact directly with BIRC5 or indirectly via downstream transcription factors of BIRC5. Overall, our study uncovered a novel 3-immune-related prognostic genes in LIHC.

Background: Immune and stromal cells in the tumor microenvironment (TME) significantly contribute to the prognosis of lung adenocarcinoma; however, the TME-related immune prognostic signature is unknown. The aim of this study was to develop a novel immune prognostic model of the TME in lung adenocarcinoma. Methods: First, the immune and stromal scores among lung adenocarcinoma patients were determined using the ESTIMATE algorithm in accordance with The Cancer Genome Atlas (TCGA) database. Differentially expressed immune-related genes (IRGs) between high and low immune/stromal score groups were analyzed, and a univariate Cox regression analysis was performed to identify IRGs significantly correlated with overall survival (OS) among patients with lung adenocarcinoma. Furthermore, a least absolute shrinkage and selection operator (LASSO) regression analysis was performed to generate TME-related immune prognostic signatures. Gene set enrichment analysis was performed to analyze the mechanisms underlying these immune prognostic signatures. Finally, the functions of hub IRGs were further analyzed to delineate the potential prognostic mechanisms in comprehensive TCGA datasets. Results: In total, 702 intersecting differentially expressed IRGs (589 upregulated and 113 downregulated) were screened. Univariate Cox regression analysis revealed that 58 significant differentially expressed IRGs were correlated with patient prognosis in the training cohort, of which three IRGs (CLEC17A, INHA, and XIRP1) were identified through LASSO regression analysis. A robust prognostic model was generated on the basis of this three-IRG signature. Furthermore, functional enrichment analysis of the high-risk-score group was performed primarily on the basis of metabolic pathways, whereas analysis of the low-risk-score group was performed primarily on the basis of immunoregulation and immune cell activation. Finally, hub IRGs CLEC17A, INHA, and XIRP1 were considered novel prognostic biomarkers for lung adenocarcinoma. These hub genes had different mutation frequencies and forms in lung adenocarcinoma and participated in different signaling pathways. More importantly, these hub genes were significantly correlated with the infiltration of CD4+ T cells, CD8+ T cells, macrophages, B cells, and neutrophils. Conclusions: The robust novel TME-related immune prognostic signature effectively predicted the prognosis of patients with lung adenocarcinoma. Further studies are required to further elucidate the regulatory mechanisms of these hub IRGs in the TME and to develop new treatment strategies.

Background Limited treatment strategies are available for squamous-cell lung cancer (SQLC) patients. Few studies have addressed whether immune-related genes (IRGs) or the tumor immune microenvironment can predict the prognosis for SQLC patients. Our study aimed to construct a signature predict prognosis for SQLC patients based on IRGs. Methods We constructed and validated a signature from SQLC patients in The Cancer Genome Atlas (TCGA) using bioinformatics analysis. The underlying mechanisms of the signature were also explored with immune cells and mutation profiles. Results A total of 464 eligible SQLC patients from TCGA dataset were enrolled and were randomly divided into the training cohort (n = 232) and the testing cohort (n = 232). Eight differentially expressed IRGs were identified and applied to construct the immune signature in the training cohort. The signature showed a significant difference in overall survival (OS) between low-risk and high-risk cohorts (P < 0.001), with an area under the curve of 0.76. The predictive capability was verified with the testing and total cohorts. Multivariate analysis revealed that the 8-IRG signature served as an independent prognostic factor for OS in SQLC patients. Naive B cells, resting memory CD4 T cells, follicular helper T cells, and M2 macrophages were found to significantly associate with OS. There was no statistical difference in terms of tumor mutational burden between the high-risk and low-risk cohorts. Conclusion Our study constructed and validated an 8-IRG signature prognostic model that predicts clinical outcomes for SQLC patients. However, this signature model needs further validation with a larger number of patients.

Background Immune-related genes (IRGs) have been confirmed to have an important role in tumorigenesis and tumor microenvironment formation. Nevertheless, a systematic analysis of IRGs and their clinical significance in soft tissue sarcoma (STS) patients is lacking. Methods Gene expression files from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) were used to select differentially expressed genes (DEGs). Differentially expressed immune-related genes (DEIRGs) were determined by matching the DEG and ImmPort gene sets, which were evaluated by functional enrichment analysis. Unsupervised clustering of the identified DEIRGs was conducted, and associations with prognosis, the tumor microenvironment (TME), immune checkpoints, and immune cells were analyzed simultaneously. Two prognostic signatures, one for overall survival (OS) and one for progression free survival (PFS), were established and validated in an independent set. Finally, two transcription factor (TF)-IRG regulatory networks were constructed, and a crucial regulatory axis was validated. Results In total, 364 DEIRGs and four clusters were identified. OS, TME scores, five immune checkpoints, and 12 types of immune cells were found to be significantly different among the four clusters. The two prognostic signatures incorporating 20 DEIRGs showed favorable discrimination and were successfully validated. Two nomograms combining signature and clinical variables were generated. The C-indexes were 0.879 (95%CI 0.832 ~ 0.926) and 0.825 (95%CI 0.776 ~ 0.874) for the OS and PFS signatures, respectively. Finally, TF-IRG regulatory networks were established, and the MYH11-ADM regulatory axis was verified in three independent datasets. Conclusion This comprehensive analysis of the IRG landscape in soft tissue sarcoma revealed novel IRGs related to carcinogenesis and the immune microenvironment. These findings have implications for prognosis and therapeutic responses, which reveal novel potential prognostic biomarkers, promote precision medicine, and provide potential novel targets for immunotherapy.

Immune-related genes (IRGs) play considerable roles in tumor immune microenvironment (IME). This research aimed to discover the differentially expressed immune-related genes (DEIRGs) based on the Cox predictive model to predict survival for lung squamous cell carcinoma (LUSC) through bioinformatics analysis. First of all, the differentially expressed genes (DEGs) were acquired based on The Cancer Genome Atlas (TCGA) using the limma R package, the DEIRGs were obtained from the ImmPort database, whereas the differentially expressed transcription factors (DETFs) were acquired from the Cistrome database. Thereafter, a TFs-mediated IRGs network was constructed to identify the candidate mechanisms for those DEIRGs in LUSC at molecular level. Moreover, Gene Ontology (GO), together with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, was conducted for exploring those functional enrichments for DEIRGs. Besides, univariate as well as multivariate Cox regression analysis was conducted for establishing a prediction model for DEIRGs biomarkers. In addition, the relationship between the prognostic model and immunocytes was further explored through immunocyte correlation analysis. In total, 3,599 DEGs, 223 DEIRGs, and 46 DETFs were obtained from LUSC tissues and adjacent non-carcinoma tissues. According to multivariate Cox regression analysis, 10 DEIRGs (including CALCB, GCGR, HTR3A, AMH, VGF, SEMA3B, NRTN, ENG, ACVRL1, and NR4A1) were retrieved to establish a prognostic model for LUSC. Immunocyte infiltration analysis showed that dendritic cells and neutrophils were positively correlated with IRGs, which possibly exerted an important part within the IME of LUSC. Our study identifies a prognostic model based on IRGs, which is then used to predict LUSC prognosis and analyze immunocyte infiltration. This may provide a novel insight for exploring the potential IRGs in the IME of LUSC.

Esophageal cancer (EC) is a serious malignant tumor, both in terms of mortality and prognosis, and immune-related genes (IRGs) are key contributors to its development. In recent years, immunotherapy for tumors has been widely studied, but a practical prognostic model based on immune-related genes (IRGs) in EC has not been established and reported. This study aimed to develop an immunogenomic risk score for predicting survival outcomes among EC patients. In this study, we downloaded the transcriptome profiling data and matched clinical data of EC patients from The Cancer Genome Atlas (TCGA) database and found 4,094 differentially expressed genes (DEGs) between EC and normal esophageal tissue (p < 0.05 and fold change >2). Then, the intersection of DEGs and the immune genes in the “ImmPort” database resulted in 303 differentially expressed immune-related genes (DEIRGs). Next, through univariate Cox regression analysis of DEIRGs, we obtained 17 immune genes related to prognosis. We detected nine optimal survival-associated IRGs (HSPA6, CACYBP, DKK1, EGF, FGF19, GAST, OSM, ANGPTL3, NR2F2) by using Lasso regression and multivariate Cox regression analyses. Finally, we used those survival-associated IRGs to construct a risk model to predict the prognosis of EC patients. This model could accurately predict overall survival in EC and could be used as a classifier for the evaluation of low-risk and high-risk groups. In conclusion, we identified a practical and robust nine-gene prognostic model based on immune gene dataset. These genes may provide valuable biomarkers and prognostic predictors for EC patients and could be further studied to help understand the mechanism of EC occurrence and development.

Medulloblastoma (MB) is one of the most common central nervous system tumors in children. At present, the vital role of immune abnormalities has been proved in tumorigenesis and progression. However, the immune mechanism in MB is still poorly understood. In the present study, 51 differentially expressed immune-related genes (DE-IRGs) and 226 survival associated immune-related genes (Sur-IRGs) were screened by an integrated analysis of multi-array. Moreover, the potential pathways were enriched by functional analysis, such as ‘cytokine–cytokine receptor interaction’, ‘Ras signaling pathway’, ‘PI3K-Akt signaling pathway’ and ‘pathways in cancer’. Furthermore, 10 core IRGs were identified from DE-IRGs and Sur-IRGs. And the potential regulatory mechanisms of core IRGs were also explored. Additionally, a new prognostic model, including 7 genes (HDGF, CSK, PNOC, S100A13, RORB, FPR1, and ICAM2) based on IRGs, was established by multivariable COX analysis. In summary, our study revealed the underlying immune mechanism of MB. Moreover, we developed a prognostic model associated with clinical characteristics and could reflect the infiltration of immune cells.

Background Testicular cancer is a very common malignancy in young men. Although testicular cancer has a high cure rate, patients have a high long-term risk of secondary malignant tumors and cardiovascular disease. In addition, for patients resistant to traditional treatment methods, new treatment methods and methods for predicting prognosis are also urgently needed. Methods Gene expression profiles of 165 normal testicular tissues and 156 testicular germ cell tumor (TGCT) tissues from GTEx database and TCGA database were used to obtain differentially expressed genes (DEGs) in TGCT. Through the ImmPort database, we obtained immune-related genes (IRGs). Univariate Cox regression analysis was used to identify prognostic IRGs. A transcription factor regulatory network was constructed to clarify the possible regulatory mechanism for the differential expression of these IRGs. Multivariate Cox regression analysis was used to establish a prognostic model. Gene expression data and related survival data of 108 TCGT patients from GEO database were used for external validation. Survival analysis, receiver operating characteristic curves (ROC) curve analysis, independent prognostic analysis, principal component analysis (PCA) and clinical correlation analysis were performed to evaluate this model. Results Three hundred and thirty-three IRGs were differentially expressed between TGCT and normal testicular tissues. We established a prognostic model (riskScore) based on 5 risk genes (SEMA6B, SEMA3G, OBP2B, INSL6 and RETN). Whether in the training cohort, the testing cohort or the entire TCGA cohort, this model could accurately stratify patients with different survival outcomes. The prognostic value of riskScore and 5 risk genes was also confirmed in the GEO database. GSEA analysis showed that DEGs in patients with better prognosis were enriched in immune-related pathways, while DEGs in patients with poorer prognosis were enriched in cancer-related pathways and cardiovascular disease-related pathways. Finally, a new Nomogram with higher prognostic value was constructed to better predict the 1-year PFS, 3-year PFS and 5-year PFS of TCGT patients. Conclusions We successfully established an immune-related risk model with high prognostic value and created a new Nomogram. We found that different immune status in tumor microenvironment may be responsible for the different survival outcomes among TGCT patients.

Aim: Bladder cancer (BC) is one of the most common malignancies with poor prognosis. We aimed to identify a genetic signature for predicting the prognosis of BC. Materials & methods: Kaplan–Meier survival and Cox regression analyses were used to construct a prognostic signature using data from The Cancer Genome Atlas. Results: Fifty four upregulated and 47 downregulated immune-related genes (IRGs) were identified in BC. A prognostic signature based on the expression of five IRGs was determined, which was moderately accurate in the prognosis of tumors. The prognostic signature was correlated with tumor stage, tumor burden and lymph node metastasis. The expression of IRGs were associated with immune infiltration. Conclusion: We determined a five gene signature, which correlates with the prognosis of BC patients, providing additional information for effective treatment.

Background: Clear cell renal cell carcinoma (ccRCC) is the most frequent and terminal subtype of RCC. Reliable markers associated with the immune response are not available to predict the prognosis of patients with ccRCC. We exploited the extensive number of ccRCC samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repository to perform a comprehensive analysis of immune-related genes (IRGs). Methods: Based on TCGA data, we incorporated IRGs and their expression profiles of 72 normal and 539 ccRCC samples. Univariate Cox analysis was used to evaluate the relationship between overall survival (OS) and IRGs expression. The Lasso Cox regression model identified prognostic genes used to establish a clinical immune prognostic model. The TF-IRG network was used to study the potential molecular mechanisms of action and properties of ccRCC-specific IRGs. Multivariate Cox analysis established a clinical prognostic model of IRGs. Results: We found a significant correlation among 15 differentially expressed IRGs with the OS of patients with ccRCC. Gene function enrichment analysis showed that these IRGs are significantly associated with response to receptor ligand activity. Lasso Cox regression analysis identified 10 genes with the greatest prognostic value. A clinical prognostic model based on six IRGs, which performed well for predicting prognosis, revealed significant associations of patients' survival with age, sex, stage, tumor, node, and metastasis. Moreover, these findings reflect the infiltration of tumors by various immune cells. Conclusion: We identified six clinically significant IRGs and incorporated them into a clinical prognostic model with great significance for monitoring and predicting prognosis of ccRCC.

Clear cell renal cell carcinoma (ccRCC) is one of the most common histological subtypes of renal cancer, with a poor prognosis. Our study aimed to identify a biomarker that is significantly associated with ccRCC prognosis and novel immunotherapeutic targets, as well as some novel molecular drugs for ccRCC. Based on the overlap of The Cancer Genome Atlas (TCGA)-Kidney Renal Clear Cell Carcinoma (KIRC) data and the ImmPort database, we obtained 1,292 immune-related genes (IRGs) and constructed a weighed co-expression network based on the IRGs. A total of 39 hub genes were screened out in three modules. CTLA4, which had the highest connectivity degree among the screened genes in a protein–protein interaction network (degree = 24), was selected. Internal validation based on the GEPIA database revealed that patients with a higher expression of CTLA4 had a significantly shorter overall survival time and disease-free survival time. Expression of CTLA4 was also closely correlated with local recurrence, pathologic stage, and immune infiltration level. External validation based on the Oncomine database and merged microarray-acquired dataset validated the mRNA expression level of hub genes. Gene-set enrichment analysis revealed that six KEGG signaling pathways, which were significantly associated with CTLA4, were enriched on immune-related pathways. Further analysis according to the TIMER database demonstrated that CTLA4 expression was positively related to dendritic cells (cor = 0.446, P = 1.32E-23) and negatively associated with tumor purity (cor = −0.267, P = 5.51E-09). Finally, we screened out 293 differentially expressed genes by integrating six datasets from the GEO database. The Connectivity Map (CMap) analysis revealed the strong potential of three small molecule drugs (monensin, quercetin, and fenbufen) for ccRCC treatment. In conclusion, CTLA4 was identified and validated in prognosis of ccRCC. CTLA4 may be a new prognostic biomarker and immunotherapeutic target for ccRCC. Monensin, quercetin, and fenbufen may be novel choices for ccRCC treatment.

Background: Due to the heterogeneity of hepatocellular carcinoma (HCC), hepatocelluarin-associated differentially expressed genes were analyzed by bioinformatics methods to screen the molecular markers for HCC prognosis and potential molecular targets for immunotherapy. Methods: RNA-seq data and clinical follow-up data of HCC were downloaded from The Cancer Genome Atlas (TCGA) database. Multivariate Cox analysis and Lasso regression were used to identify robust immunity-related genes. Finally, a risk prognosis model of immune gene pairs was established and verified by clinical features, test set and Gene Expression Omnibus (GEO) external validation set. Results: A total of 536 immune-related gene (IRGs) were significantly associated with the prognosis of patients with HCC. Ten robust IRGs were finally obtained and a prognostic risk prediction model was constructed by feature selection of Lasso. The risk score of each sample is calculated based on the risk model and is divided into high risk group (Risk-H) and low risk group (Risk-L). Risk models enable risk stratification of samples in training sets, test sets, external validation sets, staging and subtypes. The area under the curve (AUC) in the training set and the test set were all >0.67, and there were significant overall suvival (OS) differences between the Risk-H and Risk-L samples. Compared with the published four models, the traditional clinical features of Grade, Stage and Gender, the model performed better on the risk prediction of HCC prognosis. Conclusion: The present study constructed 10-gene signature as a novel prognostic marker for predicting survival in patients with HCC.

Lung squamous cell carcinoma (LSCC) is the most common subtype of non-small cell lung cancer. Immunotherapy has become an effective treatment in recent years, while patients showed different responses to the current treatment. It is vital to identify the potential immunogenomic signatures to predict patient' prognosis. The expression profiles of LSCC patients with the clinical information were downloaded from TCGA database. Differentially expressed immune-related genes (IRGs) were extracted using edgeR algorithm, and functional enrichment analysis showed that these IRGs were primarily enriched in inflammatory- and immune-related processes. "Cytokine-cytokine receptor interaction" and "PI3K-AKT signaling pathway" were the most enriched KEGG pathways. 27 differentially expressed IRGs were significantly correlated with the overall survival (OS) of patients using univariate Cox regression analysis. A prognostic risk signature that comprises seven IRGs (GCCR, FGF8, CLEC4M, PTH, SLC10A2, NPPC, and FGF4) was developed with effective predictive performance by multivariable Cox stepwise regression analysis. Most importantly, the signature could be an independent prognostic predictor after adjusting for clinicopathological parameters, and also validated in two independent LSCC cohorts (GSE4573 and GSE17710). Potential molecular mechanisms and tumor immune landscape of these IRGs were investigated through computational biology. Analysis of tumor infiltrating lymphocytes and immune checkpoint molecules revealed distinct immune landscape in high- and low-risk group. The study was the first time to construct IRG-based immune signature in the recognition of disease progression and prognosis of LSCC patients.

Background Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC. Methods RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT. Results A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment. Conclusions Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

EBV-associated gastric cancer (EBVaGC) is characterized by high frequency of DNA methylation. In this study, we investigated how epigenetic alteration of host genome contributes to pathogenesis of EBVaGC through the analysis of transcriptomic and epigenomic datasets from NIH TCGA (The Cancer Genome Atlas) consortium. We identified that immune related genes (IRGs) is a group of host genes preferentially silenced in EBV-positive gastric cancers through DNA hypermethylation. Further functional characterizations of selected IRGs reveal their novel antiviral activity against not only EBV but also KSHV. In particular, we showed that metallothionein-1 (MT1) and homeobox A (HOXA) gene clusters are down-regulated via EBV-driven DNA hypermethylation. Several MT1 isoforms suppress EBV lytic replication and release of progeny virions as well as KSHV lytic reactivation, suggesting functional redundancy of these genes. In addition, single HOXA10 isoform exerts antiviral activity against both EBV and KSHV. We also confirmed the antiviral effect of other dysregulated IRGs, such as IRAK2 and MAL, in scenario of EBV and KSHV lytic reactivation. Collectively, our results demonstrated that epigenetic silencing of IRGs is a viral strategy to escape immune surveillance and promote viral propagation, which is overall beneficial to viral oncogenesis of human gamma-herpesviruses (EBV and KSHV), considering that these IRGs possess antiviral activities against these oncoviruses., Author summary Epstein-Barr virus (EBV), one of the human gamma-herpesviruses, is a well-defined viral agent that strongly associates with malignancies of lymphoid and epithelial origin. EBV-associated gastric carcinoma (EBVaGC) is the most common malignancy caused by EBV infection. In this paper, we identified that beyond tumor suppressor genes, immune related genes (IRGs) are another group of host genes that are extensively DNA hypermethylated and epigenetically silenced in EBVaGC comparing to non-EBV GC. For certain IRGs, such as metallothionein-1 (MT1) and homeobox A (HOXA) genes, the whole gene clusters undergo DNA hypermethylation in EBVaGC, while it also occurs for isolated individual IRGs, such as IRAK2 and MAL. Considering the critical role of innate immunity in antiviral control, we expected that silencing of IRGs would benefit EBV viral replication and propagation. Indeed, our further investigation of several IRGs (MT1G, HOXA10, IRAK2, and MAL) confirmed their antiviral activities against EBV lytic replication. Some of them also suppress lytic replication of another human gamma-herpesvirus, Kaposi’s sarcoma-associated herpesvirus (KSHV), indicating their broad antiviral spectrum. Collectively, our results demonstrated that epigenetic silencing of antiviral IRGs is an efficient viral strategy utilized by oncoviruses, such as EBV, to escape immune surveillance and promote viral propagation, which is overall beneficial to viral oncogenesis.

Toxoplasma gondii rhoptry protein TgROP18 is a polymorphic virulence effector that targets immunity-related GTPases (IRGs) in rodents. Given that IRGs are uniquely diversified in rodents and not in other T. gondii intermediate hosts, the role of TgROP18 in manipulating non-rodent cells is unclear. Here we show that in human cells TgROP18I interacts with the interferon-gamma-inducible protein N-myc and STAT interactor (NMI) and that this is a property that is unique to the type I TgROP18 allele. Specifically, when expressed ectopically in mammalian cells only TgROP18I co-immunoprecipitates with NMI in IFN-γ-treated cells, while TgROP18II does not. In parasites expressing TgROP18I or TgROP18II, NMI only co-immunoprecipitates with TgROP18I and this is associated with allele-specific immunolocalization of NMI on the parasitophorous vacuolar membrane (PVM). We also found that TgROP18I reduces NMI association with IFN-γ-activated sequences (GAS) in the IRF1 gene promoter. Finally, we determined that polymorphisms in the C-terminal kinase domain of TgROP18I are required for allele-specific effects on NMI. Together, these data further define new host pathway targeted by TgROP18I and provide the first function driven by allelic differences in the highly polymorphic ROP18 locus.

Lung squamous carcinoma (LUSC) is a common subtype of lung cancer with limited available therapy and is thus associated with poor survival. Immune infiltrating cells and immune-related genes (IRGs) play a key role in the clinical outcomes of LUSC. In the present study, we aimed to develop a potential immunogenomic prognostic signature for patients with LUSC. The transcriptional profiles of 501 LUSC samples from The Cancer Genome Atlas (TCGA) and 2498 IRGs from the ImmPort database were used to develop the signature by Cox regression analysis. Ten differentially expressed and survival-associated IRGs were used to develop the risk signature, which could serve as an independent prognostic and predictive factor for patients with LUSC. Furthermore, this risk signature correlated with overall survival and clinical features, including age, in patients with LUSC. In addition, we identified 25 transcription factors that may regulate 15 survival-associated IRGs, using a regulatory network. Collectively, this immunogenomic signature could be a robust prognostic tool for patients with LUSC and holds great promise as individualized immunotherapy for LUSC.

Summary Interferon-inducible guanylate-binding proteins (GBPs) promote cell-intrinsic defense through host cell death. GBPs target pathogens and pathogen-containing vacuoles and promote membrane disruption for release of microbial molecules that activate inflammasomes. GBP1 mediates pyroptosis or atypical apoptosis of Salmonella Typhimurium (STm)- or Toxoplasma gondii (Tg)- infected human macrophages, respectively. The pathogen-proximal detection-mechanisms of GBP1 remain poorly understood, as humans lack functional immunity-related GTPases (IRGs) that assist murine Gbps. Here, we establish that GBP1 promotes the lysis of Tg-containing vacuoles and parasite plasma membranes, releasing Tg-DNA. In contrast, we show GBP1 targets cytosolic STm and recruits caspase-4 to the bacterial surface for its activation by lipopolysaccharide (LPS), but does not contribute to bacterial vacuole escape. Caspase-1 cleaves and inactivates GBP1, and a cleavage-deficient GBP1D192E mutant increases caspase-4-driven pyroptosis due to the absence of feedback inhibition. Our studies elucidate microbe-specific roles of GBP1 in infection detection and its triggering of the assembly of divergent caspase signaling platforms., Graphical Abstract, Highlights • Development of two microscopy assays for microbe/microbe-containing vacuole lysis • Human GBP1 is essential for the lysis of Toxoplasma gondii vacuoles and parasites • Caspase-4 recruitment, but not cytosolic escape of Salmonella, is GBP1 dependent • Caspase-1 cleaves and inactivates GBP1 and suppresses caspase-4-driven pyroptosis, Fisch et al. find that GBP1 targets Toxoplasma vacuolar and parasite membranes for disruption of both membranes. In contrast, appearance of cytosolic Salmonella is GBP1 independent, but caspase-4 recruitment to bacteria and activation is GBP1 dependent. In a negative feedback loop, caspase-1 cleaves GBP1 and suppresses caspase-4-driven pyroptosis during Salmonella infection.

Immune system dysfunction is associated with head and neck squamous cell carcinoma (HNSCC) development and progression and immune checkpoint inhibitors have demonstrated substantial survival benefits in platinum-refractory HNSCC, therefore, we examined the prognostic value of immune-related gene (IRG) expression in HNSCC. We analyzed the expression of 82 IRGs in 71 patients with HNSCC enrolled in a feasibility study for a prospective HNSCC biomarker-driven umbrella trial (Korean Cancer Study Group TRIUMPH study, NCT03292250). CD200R1 was identified as an independent prognostic factor and validated in GEO and TCGA database. CD2000R1 mRNA expression was found to be an independent favorable prognostic factor in patients with HNSCC. Moreover, CD200R1 was found to affect genes and pathways associated with the immune response, while seven differentially expressed genes (CD8A, DOK2, CX3CR1, TYROBP, CXCL9, CD300LF, IFNG) were associated with CD200R1 expression. Samples with higher CD200R1 expression displayed higher tumor-infiltrating immune cell counts both in silico and in histological analysis. These findings will help in the development of more accurate prognostic tools and suggest CD200R1 modulation as a HNSCC immunotherapy.

Cervical cancer and endometrial cancer remain serious threats to women's health. Even though some patients can be treated with surgery plus chemoradiotherapy as a conventional option, the overall efficacy is deemed unsatisfactory. As such, the development for new treatment approaches is truly necessary. In recent years, immunotherapy has been widely used in clinical practice and it is an area of great interest that researchers are keeping attention on. However, a thorough immune-related genes (IRGs) study for cervical cancer and endometrial cancer is still lacking. We therefore aim to make a comprehensive evaluation of IRGs through bioinformatics and large databases, and also investigate the relationship between the two types of cancer. We reviewed the transcriptome RNAs of IRGs and clinical data based on the TCGA database. Survival-associated IRGs in cervical/endometrial cancer were identified using univariable and multivariable Cox proportional-hazard regression analysis for developing an IRG signature model to evaluate the risk of patients. In the end, this model was validated based on the enrichment analyses through GO, KEGG, and GSEA pathways, Kaplan-Meier survival curve, ROC curves, and immune cell infiltration. Our results showed that out of 25/23 survival-associated IRGs for cervical/endometrial cancer, 13/12 warranted further examination by multivariate Cox proportional-hazard regression analysis and were selected to develop an IRGs signature model. As a result, enrichment analyses for high-risk groups indicated main enriched pathways were associated with tumor development and progression, and statistical differences were found between high-risk and low-risk groups as shown by Kaplan-Meier survival curve. This model could be used as an independent measure for risk assessment and was considered relevant to immune cell infiltration, but it had nothing to do with clinicopathological characteristics. In summary, based on comprehensive analysis, we obtained the IRGs signature model in cervical cancer (LTA, TFRC, TYK2, DLL4, CSK, JUND, NFATC4, SBDS, FLT1, IL17RD, IL3RA, SDC1, PLAU) and endometrial cancer (LTA, PSMC4, KAL1, TNF, SBDS, HDGF, LTB, HTR3E, NR2F1, NR3C1, PGR, CBLC), which can effectively evaluate the prognosis and risk of patients and provide justification in immunology for further researches.

Osteosarcoma (OS) is the most common malignancy of the bone that occurs majorly in young people and adolescents. Although the survival of OS patients markedly improved by complete surgical resection and chemotherapy, the outcome is still poor in patients with recurrent and/or metastasized OS. Thus, identifying prognostic biomarkers that reflect the biological heterogeneity of OS could lead to better interventions for OS patients. Increasing studies have indicated the association between immune-related genes (IRGs) and cancer prognosis. In the present study, based on the data concerning OS obtained from TARGET (Therapeutically Applicable Research to Generate Effective Treatments) database, we constructed a classifier containing 12 immune-related (IR) long non-coding RNAs (lncRNAs) and 3 IRGs for predicting the prognosis of OS by using the least absolute shrinkage and selection operation Cox regression. Besides, based on the risk score calculated by the classifier, the samples were divided into high- and low-risk groups. We further investigated the tumor microenvironment of the OS samples by ESTIMATE and CIBERSORT algorithms between the two groups. Finally, we identified three small molecular drugs with potential therapeutic value for OS patients with high-risk score. Our results suggest that the IRGs and IR-lncRNAs–based classifier could be used as a reliable prognostic predictor for OS survival.

The present study aimed to screen the immune-related genes (IRGs) in patients with liver hepatocellular carcinoma (LIHC) and construct a synthetic index for indicating the prognostic outcomes. The bioinformatic analysis was performed on the data of 374 cancer tissues and 50 normal tissues, which were downloaded from TCGA database. We observed that 17 differentially expressed IRGs were significantly associated with survival in LIHC patients. These LIHC-specific IRGs were validated with function analysis and molecular characteristics. Cox analysis was applied for constructing a RiskScore for predicting the survival. The RiskScore involved six IRGs and corresponding coefficients, which was calculated with the following formula: RiskScore = [Expression level of FABP5 *(0.064)] + [Expression level of TRAF3 * (0.198)] + [Expression level of CSPG5 * (0.416)] + [Expression level of IL17D * (0.197)] + [Expression level of STC2 * (0.036)] + [Expression level of BRD8 * (0.140)]. The RiskScore was positively associated with the poor survival, which was verified with the dataset from ICGC database. Further analysis revealed that the RiskScore was independent of any other clinical feature, while it was linked with the infiltration levels of six types of immune cells. Our study reported the survival-associated IRGs in LIHC and then constructed IRGs-based RiskScore as prognostic indicator for screening patients with high risk of short survival. Both the screened IRGs and IRGs-based RiskScore were clinically significant, which may be informative for promoting the individualized immunotherapy against LIHC.

Acute myeloid leukemia (AML) is a hematopoietic malignancy characterized by highly heterogeneous molecular lesions and cytogenetic abnormalities. Immune disorders in AML and impaired immune cell function have been found to be associated with abnormal karyotypes in AML patients. Immunotherapy has become an alternative therapeutic method that can improve the outcomes of AML patients. For solid tumors, the expression patterns of genes associated with the immune microenvironment provide valuable prognostic information. However, the prognostic roles of immune genes in AML have not been studied as yet. In this study, we identified 136 immune-related genes associated with overall survival in AML patients through a univariate Cox regression analysis using data from TCGA-AML and GTEx datasets. Next, we selected 24 hub genes from among the 136 genes based on the PPI network analysis. The 24 immune-related hub genes further underwent multivariate Cox regression analysis and LASSO regression analysis. Finally, a 6 immune-related gene signature was constructed to predict the prognosis of AML patients. The function of the hub IRGs and the relationships between hub IRGs and transcriptional factors were investigated. We found that higher levels of expression of CSK, MMP7, PSMA7, PDCD1, IKBKG, and ISG15 were associated with an unfavorable prognosis of AML patients. Meanwhile, patients in the TCGA-AML datasets were divided into a high risk score group and a low risk score group, based on the median risk score value. Patients in the high risk group tended to show poorer prognosis [P = 0.00019, HR = 1.89 (1.26–2.83)]. The area under the curve (AUC) was 0.6643. Multivariate Cox Regression assay confirmed that the 6 IRG signature was an independent prognostic factor for AML. The prognostic role of the immune related-gene signature was further validated using an independent AML dataset, GSE37642. In addition, patients in the high risk score group in the TCGA dataset were found to be of an advanced age, IDH mutation, and M5 FAB category. These results suggested that the proposed immune related-gene signature may serve as a potential prognostic tool for AML patients.

Background Colorectal cancer (CRC) is a common malignancy with high morbidity and mortality. Prognosis of CRC is highly heterogeneous which hinders the making of appropriate clinical decision tremendously. Aims We here propose to comprehensively define prognosis of CRC patients in the context of mRNA levels of immune-related genes (IRGs). Methods CRC samples were acquired from both the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). IRGs were obtained from ImmProt database. Univariate Cox-regression analysis was adopted to screen prognostically significant IRGs for CRC patients. LASSO Cox-regression analysis was used to identify the optimal prognostic IRGs and construct the prognostic model. Multivariate Cox-regression analysis was performed to determine the independence of associations of specific factors with CRC’s OS. Nomogram that combines the independent prognostic factors was constructed to predict CRC patients’ 1-year, 3-years, and 5-years OS probability. Results There were a total of 76 differentially expressed IRGs (DEGs) in CRC tumor compared with paracancerous samples. Five out of those DEGs were significantly associated with the CRC patients’ OS probability and used for the construction of prognostic model. CRC samples were divided into high-risk and low-risk group based on prognostic model. Significant differences in OS probability were obtained between high-risk and low-risk CRC patients. Additionally, a nomogram containing risk score, age and stage could effectively predict long-term OS probability of CRC patients. Conclusion In conclusion, we developed a robust IRGs-based prognostic signature which should be helpful for the understanding of heterogeneity of OS probability and the future clinical research.

The dysregulation of multiple signaling pathways, including those through endosomal Toll-like receptors (TLRs), Fc gamma receptors (FcγR), and antigen receptors in B cells (BCR), promote an autoinflammatory loop in systemic lupus erythematosus (SLE). Here, we used selective small-molecule inhibitors to assess the regulatory roles of interleukin-1 receptor (IL-1R)-associated kinase 4 (IRAK4) and Bruton's tyrosine kinase (BTK) in these pathways. The inhibition of IRAK4 repressed SLE immune complex- and TLR7-mediated activation of human plasmacytoid dendritic cells (pDCs). Correspondingly, the expression of interferon (IFN)-responsive genes (IRGs) in cells and in mice was positively regulated by the kinase activity of IRAK4. Both IRAK4 and BTK inhibition reduced the TLR7-mediated differentiation of human memory B cells into plasmablasts. TLR7-dependent inflammatory responses were differentially regulated by IRAK4 and BTK by cell type: In pDCs, IRAK4 positively regulated NF-κB and MAPK signaling, whereas in B cells, NF-κB and MAPK pathways were regulated by both BTK and IRAK4. In the pristane-induced lupus mouse model, inhibition of IRAK4 reduced the expression of IRGs during disease onset. Mice engineered to express kinase-deficient IRAK4 were protected from both chemical (pristane-induced) and genetic (NZB/W_F1 hybrid) models of lupus development. Our findings suggest that kinase inhibitors of IRAK4 might be a therapeutic in patients with SLE.

Renal cell carcinoma (RCC) is one of the most common malignancies. The immunogenomic landscape signature significantly correlates with the progression and prognosis of RCC. Novel therapeutic targets and prognostic indices in RCC are highly desirable. The TCGA database enables comprehensive immunogenomic landscape analysis. Differentially expressed immune-related genes (IRGs) were obtained from TCGA and GO analyses, and KEGG pathway analyses were performed to explore their functions and molecular mechanisms. Multivariable Cox analysis was utilized to calculate the risk score of each patient and locate survival-associated IRGs, thereby constructing a novel immune-related gene-based prognostic index (IRGPI). The correlation between IRGPI and immune cell infiltration was also investigated. A total of 41 differentially expressed IRGs were notably related to prognosis in RCC. GO functions and KEGG pathway analyses demonstrated that these genes were primarily associated with the tumour immune response and cytokine-cytokine receptor interaction pathway. An IRGPI based on seventeen survival-associated differentially expressed IRGs was constructed and exhibited a moderate predictive value in the prognosis of RCC patients and a powerful identification ability in refining the risk stratification of RCC patients. A close correlation was found between IRGPI and specific clinicopathological parameters, including age, gender, pathological stage, tumour stage, lymph node metastasis and distant metastasis. A positive correlation was found between IRGPI and the infiltration levels of neutrophils, dendritic cells, CD8+ T cells and B cells. Our results demonstrated the clinical significance and potential function of IRGs, providing additional data for prognostic risk prediction and immunotherapeutic target selection in RCC.

Objective Ankylosing spondylitis (AS) is a chronic inflammatory arthritis primarily affecting the spine and sacroiliac joints. TNF inhibitor (TNFi) drugs are recommended for patients not responding to NSAIDs; however, there is a significant need for biomarkers of response. IFN-regulated genes (IRGs) and other cytokines/chemokines are linked to autoimmune diseases and have been associated with treatment response. Our objective was to explore whether IRGs and cytokines/chemokines can be associated with response to TNFiagents in AS. Methods Peripheral blood mononuclear cells were obtained from 26 AS patients who were to receive a TNFi (I, n = 15) or placebo (P, n = 11) at week 0 and week 22. Response (R)/non-response (NR) was defined as reduction in ASDAS ≥ 1.2 points or reduction in sacroiliac/vertebral MRI lesions. The expression of 96 genes was quantified using TaqMan assays. Finally, ELISA was used to measure IL-6 in serum samples from another 38 AS patients. Results Analysis of gene expression in 26 baseline samples segregated patients into four groups defined by a signature of 15 genes (mainly IRGs). ASDAS response was associated with one group independently of treatment received. We then analysed response to the TNFi (n = 15) and identified a 12-gene signature associated with MRI response. A third IRG signature was also associated with a reduction in IRGs expression post-TNFi samples (n = 10 pairs). Finally, decreased circulating IL-6 was associated with BASDAI-R. Conclusion This pilot study suggests an association between IRG expression and response to TNFi in AS. These findings require validation in a larger cohort in order to construct predictive algorithms for patient stratification.

Background and aims Hepatocellular carcinoma (HCC) is one of the most common heterogeneous tumors that occurs after chronic liver diseases and hepatitis virus infection. Immune‐related genes (IRGs) and their ligands regulate the homeostasis of tumor microenvironment, which is essential for the treatment of HCC and its prognosis. This study aimed to investigate the clinical value of IRGs in predicting the prognosis of HCC. Methods We downloaded RNA‐seq data and clinical information from TCGA database. Samples were randomly divided into training cohort and testing cohort. The “limma” R package was performed to identify differentially expressed IRGs (DEIRGs) between HCC group and normal group. Prognostic DEIRGs (PDEIRGs) were obtained by univariate Cox analysis. LASSO and multivariate Cox analysis were used, and a prognostic risk model was constructed. In order to better demonstrate the clinical value of our model in predicting overall survival rate, a nomogram was constructed. To further investigate the molecular mechanism of our model, gene set enrichment analysis (GSEA) was performed. Results Compared with the low‐risk group, the high‐risk group had a significantly worse prognosis. Moreover, our prognostic risk model can accurately stratify tumor grade and TNM stage. Importantly, in our model, not only immune checkpoint genes were well predicted, but also human leucocyte antigen‐I molecules were revealed. GSEA suggested that “MAPK signaling pathway,” “mTOR signaling pathway,” “NOD like receptor signaling pathway,” “Toll like receptor signaling pathway,” “VEGF signaling pathway,” “WNT signaling pathway” had significant correlations with the high‐risk group. Conclusion Overall, our study showed that our prognostic risk model can be used to assess prognosis of HCC, which may provide a certain basis for the survival rate of patients with HCC.

Papillary renal cell carcinoma (PRCC) is the second most common type of renal carcinoma following clear cell renal cell carcinoma, and the role of immune-related genes (IRGs) in tumorigenesis and metastasis is evident; its prognostic value in PRCC remains unclear. In this study, we downloaded the gene expression profiles and clinical data of patients with PRCC from The Cancer Genome Atlas (TCGA) database and obtained IRGs from the ImmPort database. A total of 371 differentially expressed IRGs (DEIRGs) were discovered between PRCC and normal kidney tissues. Prognostic DEIRGs (PDEIRGs) were identified by univariate Cox regression analysis. Then, we screened the four most representative PDEIRGs (IL13RA2, CCL19, BIRC5, and INHBE) and used them to construct a risk model to predict the prognosis of patients with PRCC. This model precisely stratified survival outcome and accurately identified mutation burden in PRCC. Thus, our results suggest that these four PDEIRGs are available prognostic predictors for PRCC. They could be used to assess the prognosis and to guide individualized treatments for patients with PRCC.

BACKGROUND: Colon adenocarcinoma (COAD) is one of the most commonly diagnosed cancers, and it is closely related to the immune microenvironment. Considering that immunotherapy is not effective for all COAD patients, it is necessary to identify the effective population before administering treatment. In this study, we established an independent prognostic index based on immune-related genes (IRGs), in order to evaluate the clinical outcome of COAD. METHODS: The gene expression profiles and IRGs taken from The Cancer Genome Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort), respectively, were integrated in order to identify the differentially expressed IRGs. Functional enrichment analysis was conducted and the prognostic value of survival-related IRGs was determined. Based on Cox regression analysis, the IRG-based prognostic index (IRGPI) was established, and the model was evaluated and applied. RESULTS: A total of 51 differentially expressed survival-related IRGs were identified. The most significant signaling pathway was “cytokine-cytokine receptor interaction”. The index established herein was based on 12 survival-related IRGs, and it was highly accurate in monitoring prognosis. Moreover, the IRGPI was significantly correlated with multiple clinicopathologic factors, as well as with the infiltration of immune cells. CONCLUSIONS: An independent IRGPI was established in order to assess the immune status and tumor prognosis in COAD patients. This index can serve as a robust biomarker in clinical prognosis applications, including cancer immunotherapy.

Immune-related genes (IRGs) play regulatory roles in the immune system and are involved in the initiation and progression of colon cancer. This study aimed to develop an immunogenomic risk score for predicting survival outcomes among colon cancer patients. We analyzed the expressions of IRGs in colon specimens and discovered 484 differentially expressed IRGs when we compared specimens from colon cancer and adjacent normal tissue. Univariate Cox regression analyses were performed to identify 26 IRGs that were associated with survival. A Cox proportional hazards model with a lasso penalty identified five optimal IRGs for constructing the immunogenomic risk score (CD1B, XCL1, PLCG2, NGF, and OXTR). The risk score had good performance in predicting overall survival among patients with colon cancer and was correlated with the amount of tumor-infiltrating immune cells. Our findings suggest that the immunogenomic risk score may be useful for prognostication in colon cancer cases. Furthermore, the five IRGs included in the risk score might be useful targets for investigating the initiation of colon cancer and designing personalized treatments.

Background: The immune system plays an important role in the development of lung squamous cell carcinoma (LUSC). Therefore, immune-related genes (IRGs) expression may be an important predictor of LUSC prognosis. However, a prognostic model based on IRGs that can systematically assess the prognosis of LUSC patients is still lacking. This study aimed to construct a LUSC immune-related prognostic model by using IRGs. Methods: Gene expression data about LUSC were obtained from The Cancer Genome Atlas (TCGA). Differential expression analysis and univariate Cox regression analysis were performed to identify prognostic differentially expressed IRGs. A prognostic model was constructed using the Lasso and multivariate Cox regression analyses. Then we validated the performance of the prognostic model in training and test cohorts. Furthermore, associations with clinical variables and immune infiltration were also analyzed. Results: 593 differentially expressed IRGs were identified, and 8 of them were related to prognosis. Then a transcription factor regulatory network was established. A prognostic model consisted of 4 immune-related genes was constructed by using Lasso and multivariate Cox regression analyses. The prognostic value of this model was successfully validated in training and test cohorts. Further analysis showed that the prognostic model could be used independently to predict the prognosis of LUSC patients. The relationships between the risk score and immune cell infiltration indicated that the model could reflect the status of the tumor immune microenvironment. Conclusions: We constructed a risk model using four PDIRGs that can accurately predict the prognosis of LUSC patients. The risk score generated by this model can be used as an independent prognostic indicator. Moreover, the model can predict the infiltration of immune cells in patients, which is conducive to the prediction of patient sensitivity to immunotherapy.

Background Lung squamous cell carcinoma (LUSC) is one common type of lung cancer. Immune-related genes (IRGs) are closely associated with cancer prognosis. This study aims to screen the key genes associated with LUSC and establish an immune-related prognostic model. Methods Based on the Cancer Genome Atlas (TCGA) database, we screened the differentially expressed genes (DEGs) between LUSC and normal samples. Intersecting the DEGs with the immune-related genes (IRGs), we obtained the differentially expressed IRGs (DEIRGs). Univariate as well as multivariate Cox regression analyses were performed to identify the survival-associated IRGs and establish an immune-related prognostic model. The relationship between the prognostic model and tumor-infiltrating immune cells was analyzed by TIMER and CIBERSORT. Results A total of 229 DEIRGs were screened, and 14 IRGs associated with survival were identified using univariate Cox analysis. Among the 14 IRGs, six genes were selected out using Lasso and multivariate Cox analyses, and they were used to build the prognostic model. Further analysis indicated that overall survival (OS) of high-risk groups was lower than that of low-risk groups. High risk score was independently related to worse OS. Moreover, the risk score was positively correlated with several immune infiltration cells. Finally, the efficacy of the prognostic model was validated by another independent cohort GSE73403. Conclusion The DEIRGs described in the study may have the potential to be the prognostic molecular markers for LUSC. In addition, the risk score model could predict the OS and provides more information for the immunotherapy of patients with LUSC.

Background: Bladder cancer (BLCA) is a common malignant tumour of the urinary system. Knowledge of the mechanism of and treatment methods for BLCA remain limited. Methods: Robust rank aggregation was used to integrate five GEO bladder cancer microarray datasets for identifying differentially expressed genes (DEGs) between NMIBC and MIBC. An immune-related gene (IRG)-based prognostic index (IRGPI) was constructed using multivariate analysis. The reliability of the IRGPI was evaluated using independent datasets, and correlations between the IRGPI and clinicopathological characteristics as well as the immune microenvironment were evaluated. Finally, a nomogram was established to evaluate patient prognosis with BLCA. Findings: We identified 154 DEGs related to the degree of BLCA infiltration, including 24 IRGs. Missense mutation was the most common mutation type in IRGs. Ten hub IRGs were identified by protein-protein interaction network analysis. GSEA and GSVA of two novel BLCA-related genes (TYROBP and FCER1G) revealed that they were related to immunity. Nine survival-related IRGs were identified and their potential regulation by transcription factors was analysed. An independent IRGPI (CTSE, CXCL10, FAM3B, MMP9, OLR1, and S100P) was established, and was confirmed to be related to tumour stage, tumour grade, T stage, and N stage. The nomogram could effectively predict the prognosis of BLCA patients. Importantly, the IRGPI reflected infiltration by several types of immune cells. Interpretation: Our data provide new insights in the pathogenesis of BLCA and target genes for immunotherapy. The application of molecular markers for hierarchical prediction paves the way to precision medicine. Funding: This study was financially supported by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (grant number: 2018-I2M-1-002) and the Beijing Hospital Clinical Research 121 Project (BJ-2018-090). Declaration of Interest: The authors have declared no competing interests.

Changes in immune responses to hepatocellular carcinoma (HCC) are closely related to the occurrence, development, and prognosis of this disease. Exploring the role of immune-related genes (IRGs) in HCC would provide insights into the mechanisms regulating this disease. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) provide a platform for such research, owing to a large number of HCC samples available for comprehensive and systematic immunogenomics analyses. We analyzed the IRGs expression profile and clinical information of patients with HCC based on the TCGA and ICGC database. Potential molecular mechanisms and properties of the screened IRGs were analyzed across multiple databases. And we analyzed the correlation between IRGs, single-nucleotide polymorphisms, and copy number variation. A novel prognostic index, based on IRGs, was developed using the LASSO Cox regression algorithm, followed by univariate and multivariate Cox regression analyses to analyze the prognostic index. Information in the ICGC database was used to verify the reliability of the prognostic index. A total of 54 differentially expressed IRGs were found to be significantly associated with HCC prognosis, and there is a significant correlation between their expression level and copy number variation. Functional enrichment analyses indicated that the genes play active roles in tumor and immune-related signaling pathways. In addition, five potential biomarkers namely IRG, MAPK3, HSP90AA1, HSP90AB1, HSPA4, and CDK4, were identified. Finally, a novel prognostic index, based on IRGs (PSMD14, FABP6, ISG20L2, HGF, BIRC5, IL17D, and STC2), was found useful as an independent prognostic factor, not only for prognosis but also to reflect levels of infiltration in a variety of immune cells. Our team conducted a genomics study of IRGs in HCC and screened several clinically significant IRGs, and our model provides an effective approach for stratification and characterization of patients using IRG-based immunolabeling tools to monitor the prognosis of HCC.

Aims Head and neck squamous cell carcinoma (HNSCC) is an highly aggressive tumor with heterogeneous prognosis. We here report that immune-related genes (IRGs) could effectively distinguish prognostically different HNSCC patients. Materials and methods MRNA levels of 1333 IRGs that from ImmPort database in HNSCC samples were acquired from the Cancer Genome Atlas (TCGA). H2o, a machine learning-based R package, was used for screening the top most representative genes from the IRGs. Univariate Cox-regression analysis was performed to identify prognostically-related genes based on the randomly generated training samples from TCGA set. LASSO Cox-regression analysis was applied for the construction of prognostic model for HNSCC. A total of six IRGs were finally retained for their prognostic significance and used for LASSO Cox-regression analysis. Key findings Samples from exclusive training and testing set that randomly generated from TCGA, and another independent validation set from the Gene Expression Omnibus (GEO) were divided into high- and low-risk groups according to the prognostic model. HNSCC samples within high-risk groups have significantly inferior overall survival (OS) compared with those within low-risk groups. Differences in genomic mutation landscape and tumor infiltration immune cells also exist between the two sample groups. What's more, risk score was proved to be an independent prognostic factor for HNSCC by stratification analysis. Significance IRGs are pivotal HNSCC prognostic signatures and should be helpful for its clinical decision-making.

Background: Lung squamous cell carcinoma (LUSC) is one of the most common histological subtypes of non-small cell lung cancer (NSCLC), and its morbidity and mortality are steadily increasing. The purpose of this study was to study the relationship between the immune-related gene (IRGs) profile and the outcome of LUSC in patients by analyzing datasets from The Cancer Genome Atlas (TCGA).Methods: We obtained publicly available LUSC RNA expression data and clinical survival data from The Cancer Genome Atlas (TCGA), and filtered IRGs based on The ImmPort database. Then, we identified risk immune-related genes (r-IRGs) for model construction using Cox regression analysis and defined the risk score in this model as the immune gene risk index (IRI). Multivariate analysis was used to verify the independent prognostic value of IRI and its association with other clinicopathological features. Pearson correlation analysis was used to explore the molecular mechanism affecting the expression of IRGs and the correlation between IRI and immune cell infiltration.Results: We screened 15 r-IRGs for constructing the risk model. The median value of IRI stratified the patients and there were significant survival differences between the two groups (p = 4.271E-06). IRI was confirmed to be an independent prognostic factor (p < 0.001) and had a close correlation with the patients' age (p < 0.05). Interestingly, the infiltration of neutrophils or dendritic cells was strongly upregulated in the high-IRI groups (p < 0.05). Furthermore, by investigating differential transcription factors (TFs) and functional enrichment analysis, we explored potential mechanisms that may affect IRGs expression in tumor cells.Conclusion: In short, this study used 15 IRGs to build an effective risk prediction model, and demonstrated the significance of IRGs-based personalized immune scores in LUSC prognosis.

This study was designed to identify an immune-related gene signature (IRGS) associated with breast cancer (BC) patient outcomes. Transcriptomic data from 1411 BC patients in the TCGA and GEO databases were used to identify differentially expressed immune-related genes (DEIGs) when comparing BC tumor and normal tissue samples. We were able to construct a 27-gene IRGS that was able to effectively separate BC patients into high- and low-risk groups that corresponded to significant differences in overall and recurrence-free survival (OS and RFS, respectively). Besides, the relevance of this signature to immune response and immune cell infiltration of BC tumors was evaluated. These high- and low-risk BC patients were found to exhibit significantly different immune responses and functional enrichment. We also identified patients in the high-risk group exhibited significantly reduced immune cell infiltration of tumors relative to low-risk patients. Together, the results of this analysis offer a novel overview of the immune microenvironment within BC tumors and highlight key immunological genes associated with patient survival outcomes.

Background: Immune-related genes (IRGs) are critically involved in the tumor microenvironment (TME) of colon adenocarcinoma (COAD). Here, the study was mainly designed to establish a prognostic model of IRGs to predict the survival of COAD patients. Methods: The Cancer Genome Atlas (TCGA), Immunology Database and Analysis Portal (ImmPort) database, and Cistrome database were utilized for extracting data regarding the expression of immune gene- and tumor-related transcription factors (TFs), aimed at the identification of differentially expressed genes (DEGs), differentially expressed IRGs (DEIRGs), and differentially expressed TFs (DETFs). Univariate Cox regression analysis was subsequently performed for the acquisition of prognosis-related IRGs, followed by establishment of TF regulatory network for uncovering the possible molecular regulatory association in COAD. Subsequently, multivariate Cox regression analysis was conducted to further determine the role of prognosis-related IRGs for prognostic prediction in COAD. Finally, the feasibility of a prognostic model with immunocytes was explored by immunocyte infiltration analysis. Results: A total of 2450 DEGs, 8 DETFs, and 79 DEIRGs were extracted from the corresponding databases. Univariate Cox regression analysis revealed 11 prognosis-related IRGs, followed by establishment of a regulatory network on prognosis-related IRGs at transcriptional levels. Functionally, IRG GLP2R was negatively modulated by TF MYH11, whereas IRG TDGF1 was positively modulated by TF TFAP2A. Multivariate Cox regression analysis was subsequently performed to establish a prognostic model on the basis of seven prognosis-related IRGs (GLP2R, ESM1, TDGF1, SLC10A2, INHBA, STC2, and CXCL1). Moreover, correlation analysis of immunocyte infiltration also revealed that the seven-IRG prognostic model was positively associated with five types of immunocytes (dendritic cell, macrophage, CD4 T cell, CD8 T cell, and neutrophil), which may directly reflect tumor immune state in COAD. Conclusions: Our present findings indicate that the prognostic model based on prognosis-related IRGs plays a crucial role in the clinical supervision and prognostic prediction of COAD patients at both molecular and cellular levels.

Aim Immune-related genes are associated with the prognosis of colorectal cancer (CRC) patients. The aim of this study was to evaluate the impact of an immune-related gene signature (IRGS) in predicting the prognosis of early-stage CRC patients. Methods In total, 309 CRC patients were selected for the identification of prognostic IRGS using the CIT/GSE39582 microarray dataset. Five independent datasets including 1587 CRC patients were divided into a training cohort (n = 566) and two validation cohorts (n = 624 in validation-1 and n = 397 in meta-validation). Prognostic analyses were performed to test the predictive value of IRGS. Results A prognostic IRGS that included 23 immune-related genes was constructed and significantly stratified patients into immune low-vs. high-risk groups in terms of disease-free survival using patients with early-stage disease (I or II) in the training cohort. Similarly, a higher IRGS was correlated with significantly worse prognosis of early-stage patients in validation-1 and meta-validation cohorts. Compared with Oncotype DX colon, we found that IRGS exhibited an improved survival correlation in the training cohort. After integration with clinical characteristics, IRGS remained as an independent prognostic factor in multivariate analysis. Furthermore, IRGS-stratified immune low-risk group patients gained less benefit from adjuvant chemotherapy in the validation-1 cohort. Several biological processes, including inflammatory response, were enriched among genes in identified the immune high-risk group. Consistent with this finding, the IRGS-identified immune high-risk group exhibited significantly increased immune and stromal cell infiltration. Conclusion The proposed prognostic IRGS is a promising system for estimating DFS of colorectal cancer patients, especially those with early-stage disease.

Acute myeloid leukemia with normal karyotype (NK-AML) is a group of diseases with high heterogeneity and immunological processes are significantly associated with its initiation and development. The implication of the immunogenomic landscape in the prognosis of patients with NK-AML has remained largely elusive. In the present study, the expression profiles of immune-related genes (IRGs) were examined and their association with overall survival (OS) was determined in 60 patients with NK-AML from The Cancer Genome Atlas dataset and 104 patients from the Gene Expression Omnibus (GEO) dataset no. GSE71014. Univariate Cox regression analysis was used to identify 42 and 203 IRGs in the two respective cohorts, which were significantly associated with OS in NK-AML. A risk model was constructed based on the regression coefficient and expression values of nine survival-associated IRGs shared between the two datasets [zinc finger CCCH-type containing, antiviral 1 like; transferrin receptor; suppressor of cytokine signaling 1; ELAV like RNA binding protein 1; roundabout guidance receptor 3; unc-93 homolog B1, Toll-like receptor signaling regulator; protein tyrosine phosphatase non-receptor type 6; interleukin 2 receptor subunit alpha (IL2RA) and IL3RA]. Using this risk model, patients with NK-AML may be divided into high- and low-risk groups in prognostic predictions. The area under the receiver operating characteristic curve for predicting OS was 0.793. The prognostic role of this risk model was successfully verified in another independent cohort (GEO dataset no. GSE71014). The prognostic risk score was positively associated with age and fms related receptor tyrosine kinase 3 mutation and correlated with infiltration by T regulatory cells. In conclusion, the results of the present study provided an IRG score model for prognostic stratification of adult patients with NK-AML, as well as further insight into the implication of IRGs in NK-AML that may lead to the development of novel immunotherapy approaches for this disease.

Background Tumour cells interfere with normal immune functions by affecting the expression of some immune-related genes, which play roles in the prognosis of cancer patients. In recent years, immunotherapy for tumours has been widely studied, but a practical prognostic model based on immune-related genes in lung adenocarcinoma comparable to existing model has not been established and reported. Methods We first obtained publicly accessible lung adenocarcinoma RNA expression data from The Cancer Genome Atlas (TCGA) for differential gene expression analysis and then filtered immune-related genes based on the ImmPort database. By using the lasso algorithm and multivariate Cox Proportional-Hazards (CoxPH) regression analysis, we identified candidate genes for model development and validation. The robustness of the model was further examined by comparing the model with three established gene models. Results Gene expression data from a total of 524 lung adenocarcinoma patients from TCGA were used for model development. We identified four biomarkers (MAP3K8, CCL20, VEGFC, and ANGPTL4) that could predict overall survival in lung adenocarcinoma (HR = 1.98, 95% CI 1.48 to 2.64, P = 4.19e−06) and this model could be used as a classifier for the evaluation of low-risk and high-risk groups. This model was validated with independent microarray data and was highly comparable with previously reported gene expression signatures for lung adenocarcinoma prognosis. Conclusions In this study, we identified a practical and robust four-gene prognostic model based on an immune gene dataset with cross-platform compatibility. This model has potential value in improving TNM staging for survival predictions in patients with lung adenocarcinoma. Impact The study provides a method of immune relevant gene prognosis model and the identification of immune gene classifier for the prediction of lung adenocarcinoma prognosis with RNA sequencing and microarray compatibility.

Background: There has been no report of prognostic signature based on immune-related genes (IRGs). This study aimed to develop an IRG-based prognostic signature that could stratify patients with bladder cancer (BLCA). Methods: RNA-seq data along with clinical information on BLCA were retrieved from the Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO). Based on TCGA dataset, differentially expressed IRGs were identified via Wilcoxon test. Among these genes, prognostic IRGs were identified using univariate Cox regression analysis. Subsequently, we split TCGA dataset into the training (n=284) and test datasets (n=119). Based on the training dataset, we built a least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression model with multiple prognostic IRGs. It was validated in the training dataset, test dataset and external dataset GSE13507 (n=165). Additionally, we accessed the six types of tumor-infiltrating immune cells from Tumor Immune Estimation Resource (TIMER) website and analyzed the difference between risk groups. Further, we constructed and validated a nomogram to tailor treatment for patients with BCLA. Results: A set of 47 prognostic IRGs was identified. LASSO regression and identified seven BLCA-specific prognostic IRGs, i.e., RBP7, PDGFRA, AHNAK, OAS1, RAC3, EDNRA, and SH3BP2. We developed an IRG-based prognostic signature that stratify BLCA patients into two subgroups with statistically different survival outcomes (hazard ratio (HR) = 10, 95% confidence interval (CI) = 5.6-19, P < 0.001). The ROC curve analysis showed acceptable discrimination with AUCs of 0.711, 0.754 and 0.772 at 1-year, 3-year and 5-year follow-up respectively. The predictive performance was validated in the train set, test set, and external dataset GSE13507. Besides, the increased infiltration of CD4+ T cells, CD8+ T cells, macrophage, neutrophil, and dendritic cells in the high-risk group (as defined by the signature) indicated chronic inflammation may reduce the survival chances of BLCA patients. The nomogram demonstrated to be clinically-relevant and effective with accurate prediction and positive net benefit. Conclusion: The present immune-related signature can effectively classify BLCA patients into high-risk and low-risk groups in terms of survival rate, which may help select high-risk BLCA patients for more intensive treatment.

Background. Triple-negative breast cancer (TNBC) is usually poorly differentiated, highly invasive, susceptible to distant metastasis, and less responsive to endocrine and targeted therapy. However, immunotherapy is a promising treatment for TNBC patients recently. Methods. The prognostic value of immune-related genes (IRGs) was explored by using RNA sequencing and microarray data of 123 and 107 TNBC patients from TCGA and GEO databases, respectively. Results. In TCGA database, GO and KEGG pathway analysis of 119 differential IRGs indicated that they actively participate in the interaction of cytokines and receptors. A nomogram model constructed by the prognosis-related CCL25, IL29, TDGF3, GPR44, and GREM2 in the IRGs could personalize and visualize the 1-, 2-, 3-, 4-, and 5-year overall survival (OS) of TNBC patients. Moreover, TNBC patients could be defined as low-risk ( risk score < 194 ) or high-risk ( risk score ≥ 194 ) cohorts based on the risk score derived from the nomogram model. The results could be validated by the GSE58812 dataset. Furthermore, the risk score was an independent risk factor for TNBC patients ( HR = 1.019 , 95% CI 1.012-1.027, p < 0.001 ) and was positively related to stage ( p = 0.017 ). Interestingly, the risk score could reflect the infiltration of B cells, CD4+ T cells, CD8+ T cells, dendritic cells, and neutrophils. Conclusion. These findings provided a reference for personalized OS prediction in TNBC patients and might be potential immune biomarkers for designing novel therapy.

Interferon-inducible Guanylate binding proteins (GBPs) promote cell-intrinsic defense through host cell death. GBPs target pathogens and pathogen-containing vacuoles, and assist in membrane-disruption for release of microbial molecules that trigger inflammasome activation. GBP1 mediates atypical macrophage apoptosis or pyroptosis infected with Toxoplasma gondii or Salmonella Typhimurium, respectively. The pathogen-proximal detection-mechanisms of GBP1 are poorly understood, since humans lack functional Immunity-related GTPases (IRGs) which control mGbps in mice. Here, we describe two new assays showing that GBP1 promotes lysis of Tg-containing vacuoles and parasite plasma membranes, releasing Tg-DNA. In contrast, we show GBP1 targets cytosolic STm, recruits caspase-4 to the bacterial surface for its activation by LPS but does not contribute to bacterial vacuole escape. During STm infection, caspase-1 cleaves and inactivates GBP1, and expression of cleavage-deficient GBP1D192E mutant increased pyroptosis through absence of feedback inhibition. Our studies elucidate microbe-specific roles of GBP1 in infection-detection triggering assembly of divergent caspase signaling platforms.

Background: Immune-related genes (IRGs) are key contributors to bladder cancer (BLCA) development. Whether IRGs-based prognostic signature can predict the prognosis of BLCA patients deserves to be investigated. Methods: Data were downloaded from the Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) database. The differentially expressed IRGs (DEIRGs) were analyzed through Wilcoxon test. Univariate Cox regression analysis was used to identify the prognostic DEIRGs (PDEIRGs), and multivariate Cox regression analysis was employed to construct a prognostic model. The predictive accuracy and independent forecasting ability were evaluated. Tumor-infiltrating immune cells (TIICs) were obtained through Tumor Immune Estimation Resource (TIMER) website and CIBERSORT method. The correlations between present prognostic signature and clinical variables or TIICs were explored. Furthermore, a transcription factors (TFs)-based regulatory network and enrichment analysis were performed to explore the underlying mechanism. Findings: 3126 DEGs, 261 DEIRGs and 24 PDEIRGs were identified. Ten optimal PDEIRGs were adopted to construct a prognostic model, which can serve as an independent prognostic predictor with excellent predictive accuracy. This model could reflect the clinical variables and TIICs, which further verified its reliability in BLCA prognosis prediction. Besides, several immune-related GO terms and KEGG pathways were significantly enriched, and TFs might involve with PDEIRGs regulating. Interpretation: We find a valuable PDEIRGs-based prognostic signature for BLCA patients and reveal the potential regulatory mechanism. Funding Statement: This study was supported by grants from the National Key Basic Research Development Program (973 Program) (No. 2013CB530500), the National Natural Science Foundation of China (No. 81372225, 81372771, 81772734, 81802935) and the Innovation Capability Support Plan of Shaanxi Province (2020PT-021). Declaration of Interests: The authors declare that they have no competing interests.